Abstract

Groundwater exploration has become increasingly important in Pakistan due to rapid urbanization, climate variability, and growing water demand. This study presents a geophysical well logging investigation conducted at the Khasala Service Area along the Rawalpindi Ring Road, Pakistan, to evaluate subsurface lithology and groundwater potential. The borehole was drilled to a total depth of 515 ft (157m), and Short Normal (SN), Long Normal (LN), and Spontaneous Potential (SP) geophysical logs were integrated with lithological observations to delineate groundwater-bearing formations. The subsurface profile revealed alternating sequences of clay, sand, gravel, boulders, sandstone, and shale, with five productive aquifer zones identified at depths of 141–164ft, 184–249ft, 295–361ft, 410–450ft and 460–480ft. Based on the integrated interpretation of lithological and geophysical data, a preliminary groundwater yield of approximately 4000–5000 gallons per hour (gph) was inferred, indicating moderate to good groundwater potential for future commercial and municipal water supply development. However, this estimate should be regarded as a preliminary hydrogeological assessment and requires validation through pumping tests, groundwater-level monitoring, and hydraulic analyses to establish sustainable groundwater abstraction rates. Furthermore, the study highlights the potential of integrating Artificial Intelligence (AI), remote sensing, and Geographic Information Systems (GIS) with conventional geophysical well logging to improve lithological classification, aquifer delineation, groundwater yield prediction, and regional groundwater potential assessment. The proposed multidisciplinary framework provides a reliable and sustainable approach for groundwater exploration, hydrogeological characterization, and groundwater resource management, particularly in rapidly developing infrastructure corridors and other semi-arid regions experiencing increasing water demand.

Keywords:Groundwater; Geophysical logging; Artificial intelligence; Aquifer; Resistivity survey; Hydrogeology; Borehole investigation

Highlights
A. Integrated geophysical logging with AI for groundwater exploration
B. Identified multiple aquifer zones in a 515 ft borehole profile
C. AI improved lithology classification and aquifer prediction accuracy
D. Proposed AI framework for sustainable groundwater management and drilling